Lab 02: Introduction to R Part 2
Markdown Author: Jessie Bell, 2023
Libraries Used: dplyr
1. Peeing in Pools
A. Vector
poolpee <- c(640, 1070, 780, 70, 160, 130, 60, 50, 2110, 70, 350, 30, 210, 90, 470, 580, 250, 310, 460, 430, 140, 1070, 130)B. Mean
mean(poolpee) #ng/L## [1] 420C. Iterate a List
poolpee_conversion <- poolpee/4000 # urine/L
print(poolpee_conversion)## [1] 0.1600 0.2675 0.1950 0.0175 0.0400 0.0325 0.0150 0.0125 0.5275 0.0175
## [11] 0.0875 0.0075 0.0525 0.0225 0.1175 0.1450 0.0625 0.0775 0.1150 0.1075
## [21] 0.0350 0.2675 0.0325D. Converted Mean
mean(poolpee_conversion) # the value is much lower after converting## [1] 0.105E. Double Check
sum(poolpee_conversion)/length(poolpee_conversion)## [1] 0.105mean(poolpee_conversion) ## [1] 0.105# they are exactly the same!F. Dimensionally Analyze
# our dimensions for poolpee_conversion are in urine/L, so to determine how much urine is in 500,000 liter pool on average we can just multiply the mean by 500,000 liters to cancel out liters.
mean(poolpee_conversion)*500000 # Urine is our new unit## [1] 525002. Weddell Seals
A. Vector
# metabolic costs for dives in ml of O2/kg
feedingDives <- c(71.0, 77.3, 82.6, 96.1, 106.6, 112.8, 121.2, 126.4, 127.5, 143.1)
nonfeedingDives <- c(42.2, 51.7, 59.8, 66.5, 81.9, 82.0, 81.3, 81.3, 96.0, 104.1)B. Length
length(feedingDives)## [1] 10length(nonfeedingDives)## [1] 10# both lists have 10 sealsC. Metabolism Difference
# assuming the lists are organized:
MetabolismDifference <- feedingDives-nonfeedingDives
print(MetabolismDifference)## [1] 28.8 25.6 22.8 29.6 24.7 30.8 39.9 45.1 31.5 39.0D. Average Difference
avg_feeding <- mean(feedingDives)
avg_nonfeeding <- mean(nonfeedingDives)
avg_feeding - avg_nonfeeding## [1] 31.78E. Ratio
oxygenconsumption_ratio <- feedingDives/nonfeedingDives
oxygenconsumption_ratio## [1] 1.682464 1.495164 1.381271 1.445113 1.301587 1.375610 1.490775 1.554736
## [9] 1.328125 1.374640F. Log of the Ratio
logratio <- log(oxygenconsumption_ratio)
logratio## [1] 0.5202597 0.4022362 0.3230040 0.3681874 0.2635845 0.3188971 0.3992961
## [8] 0.4413055 0.2837682 0.3181917mean(logratio)## [1] 0.3638733. Countries Data
A. Read in the Data
countriesData <- read.csv("countries.csv")B. Look at Data
summary(countriesData)## country total_population_in_thousands_2015
## Length:196 Min. : 1.6
## Class :character 1st Qu.: 1875.8
## Mode :character Median : 8069.6
## Mean : 37721.9
## 3rd Qu.: 26413.0
## Max. :1400000.0
## NA's :2
## gross_national_income_per_capita_2013 life_expectancy_at_birth_female
## Min. : 600 Min. :48.80
## 1st Qu.: 3070 1st Qu.:67.05
## Median : 9800 Median :75.90
## Mean : 14792 Mean :73.42
## 3rd Qu.: 20370 3rd Qu.:79.25
## Max. :123860 Max. :86.70
## NA's :27 NA's :13
## life_expectancy_at_birth_male life_expectancy_at_age_60_female
## Min. :47.40 Min. :12.70
## 1st Qu.:62.90 1st Qu.:18.00
## Median :69.80 Median :20.40
## Mean :68.53 Mean :20.81
## 3rd Qu.:73.95 3rd Qu.:23.40
## Max. :81.10 Max. :28.60
## NA's :13 NA's :13
## life_expectancy_at_age_60_male physicians_density_per_1000
## Min. :12.50 Min. :0.029
## 1st Qu.:15.80 1st Qu.:1.681
## Median :17.50 Median :2.765
## Mean :18.07 Mean :2.725
## 3rd Qu.:20.20 3rd Qu.:3.510
## Max. :23.90 Max. :7.519
## NA's :13 NA's :125
## number_neonatal_deaths_in_thousands_2014 measles_immunization_oneyearolds
## Min. : 0.00 Min. :22.00
## 1st Qu.: 0.00 1st Qu.:83.25
## Median : 1.00 Median :93.00
## Mean : 14.11 Mean :87.28
## 3rd Qu.: 9.50 3rd Qu.:97.00
## Max. :722.00 Max. :99.00
## NA's :2 NA's :2
## dpt2_vaccination_oneyearolds fines_for_tobacco_advertising_2014
## Min. :20.00 Length:196
## 1st Qu.:84.25 Class :character
## Median :94.00 Mode :character
## Mean :87.91
## 3rd Qu.:97.00
## Max. :99.00
## NA's :2
## mortality_rate_cancer_2012 cigarette_price_2014 continent
## Min. : 54.00 Min. : 0.360 Length:196
## 1st Qu.: 88.62 1st Qu.: 1.320 Class :character
## Median :108.00 Median : 2.620 Mode :character
## Mean :109.64 Mean : 3.798
## 3rd Qu.:124.53 3rd Qu.: 4.965
## Max. :223.00 Max. :16.140
## NA's :24 NA's :89
## ecological_footprint_2000 ecological_footprint_2012
## Min. : 0.600 Min. :0.700
## 1st Qu.: 1.097 1st Qu.:1.400
## Median : 2.140 Median :2.000
## Mean : 3.147 Mean :2.353
## 3rd Qu.: 4.872 3rd Qu.:3.000
## Max. :15.990 Max. :5.300
## NA's :58 NA's :147
## cell_phone_subscriptions_per_100_people_2012
## Min. : 5.47
## 1st Qu.: 69.83
## Median :103.25
## Mean : 99.90
## 3rd Qu.:126.10
## Max. :198.62
## NA's :10# country
# total_population_in_thousands_2015
# gross_national_income_per_capita_2013C. African Countries
AfricanCountries <- subset(countriesData, continent == "Africa")
length(AfricanCountries)## [1] 18# there are 18 countries located in AfricaD. Variable Type
summary(countriesData)## country total_population_in_thousands_2015
## Length:196 Min. : 1.6
## Class :character 1st Qu.: 1875.8
## Mode :character Median : 8069.6
## Mean : 37721.9
## 3rd Qu.: 26413.0
## Max. :1400000.0
## NA's :2
## gross_national_income_per_capita_2013 life_expectancy_at_birth_female
## Min. : 600 Min. :48.80
## 1st Qu.: 3070 1st Qu.:67.05
## Median : 9800 Median :75.90
## Mean : 14792 Mean :73.42
## 3rd Qu.: 20370 3rd Qu.:79.25
## Max. :123860 Max. :86.70
## NA's :27 NA's :13
## life_expectancy_at_birth_male life_expectancy_at_age_60_female
## Min. :47.40 Min. :12.70
## 1st Qu.:62.90 1st Qu.:18.00
## Median :69.80 Median :20.40
## Mean :68.53 Mean :20.81
## 3rd Qu.:73.95 3rd Qu.:23.40
## Max. :81.10 Max. :28.60
## NA's :13 NA's :13
## life_expectancy_at_age_60_male physicians_density_per_1000
## Min. :12.50 Min. :0.029
## 1st Qu.:15.80 1st Qu.:1.681
## Median :17.50 Median :2.765
## Mean :18.07 Mean :2.725
## 3rd Qu.:20.20 3rd Qu.:3.510
## Max. :23.90 Max. :7.519
## NA's :13 NA's :125
## number_neonatal_deaths_in_thousands_2014 measles_immunization_oneyearolds
## Min. : 0.00 Min. :22.00
## 1st Qu.: 0.00 1st Qu.:83.25
## Median : 1.00 Median :93.00
## Mean : 14.11 Mean :87.28
## 3rd Qu.: 9.50 3rd Qu.:97.00
## Max. :722.00 Max. :99.00
## NA's :2 NA's :2
## dpt2_vaccination_oneyearolds fines_for_tobacco_advertising_2014
## Min. :20.00 Length:196
## 1st Qu.:84.25 Class :character
## Median :94.00 Mode :character
## Mean :87.91
## 3rd Qu.:97.00
## Max. :99.00
## NA's :2
## mortality_rate_cancer_2012 cigarette_price_2014 continent
## Min. : 54.00 Min. : 0.360 Length:196
## 1st Qu.: 88.62 1st Qu.: 1.320 Class :character
## Median :108.00 Median : 2.620 Mode :character
## Mean :109.64 Mean : 3.798
## 3rd Qu.:124.53 3rd Qu.: 4.965
## Max. :223.00 Max. :16.140
## NA's :24 NA's :89
## ecological_footprint_2000 ecological_footprint_2012
## Min. : 0.600 Min. :0.700
## 1st Qu.: 1.097 1st Qu.:1.400
## Median : 2.140 Median :2.000
## Mean : 3.147 Mean :2.353
## 3rd Qu.: 4.872 3rd Qu.:3.000
## Max. :15.990 Max. :5.300
## NA's :58 NA's :147
## cell_phone_subscriptions_per_100_people_2012
## Min. : 5.47
## 1st Qu.: 69.83
## Median :103.25
## Mean : 99.90
## 3rd Qu.:126.10
## Max. :198.62
## NA's :10#continents: categorical
#cell_phone_subscriptions_per_100_people_2012: numerical
#total_population_in_thousands_2015: numerical
#fines_for_tobacco_advertising_2014: categorical
# notice that some of the columns print right next to one another so it can be difficult to tell where one column ends and another begins. E. Add New Column
countriesData$mean_difference = (countriesData$ecological_footprint_2012-countriesData$ecological_footprint_2000)
mean(countriesData$mean_difference)## [1] NA4. New Data Frame
# I subset AfricanCountries already in problem 3 C.
sum(AfricanCountries$total_population_in_thousands_2015) #total population## [1] 11845015. Extension Activity, add tabs to your markdown
Add interactive tabs like the ones above to your markdown by adding {.tabset} to one of your headers like this three hashtag header:
Now all of your headings with four hashtags should be interactive. Learn more about how to do this here.
This lab was completed using the following textbook: The Analysis of Biological Data by Whitlock and Schluter